{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=[1,2,3,4,5]\n",
    "y=[50,55,40,48,43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rl7n7FHe/XsWGyOaVWAk3v3wzT7yt/hwiUlySauFw9wYzOw24IUN5RAretLOm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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25bd6a7c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Day')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Weather Chart')\n",
    "lines = plt.plot(x,y,color=\"Green\", linewidth=1.0, linestyle=\":\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Google \"matplotlib line properties\" and it will show you page listing all properties. Usually this page: http://matplotlib.org/users/pyplot_tutorial.html"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
